Home

Row

Tweets Today

96

Tweeters Today

14

#rstats Likes

2076145

#rstats Tweets

219719

Row

Tweet volume

Tweets by Hour of Day

Row

💗 Most Liked Tweet Today

✨ Most Retweeted Tweet Today

🎉 Most Recent

Rankings

Row

Top Tweeters

User Engagement/Tweet
@CloarecJulien 4644.444
@v_matzek 2453.000
@TheToadLady 1602.500
@kiramhoffman 1138.000
@OwenOzier 959.000
@kaymwilliamson 943.000
@_johntlovell 936.000
@math_lehot 930.200
@SebastienPolis 875.000
@TechAmazing 797.000

Where Engagement is RT * 2 + Favourite

Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters that also have the hashtag.

Row

Top Words

Top Locations

Row

Top Hashtags

Hashtag Count
#Python 91783
#DataScience 89588
#AI 74315
#Analytics 71092
#IoT 63712
#MachineLearning 61820
#BigData 61280
#IIoT 56164
#TensorFlow 53557
#Linux 53363

Excluding #rstats and similar variations

Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

Data

Tweets in the current week

---
title: "#rstats Twitter Explorer"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
    theme:
      version: 4
      bootswatch: yeti
    css: styles/main.css
---

```{r load_proj, include=FALSE}
devtools::load_all()
```

```{r load_packages, include=FALSE, cache=TRUE}
library(flexdashboard)
library(rtweet)
library(dplyr)
library(stringr)
library(tidytext)
library(lubridate)
library(echarts4r)
library(DT)

rstats_tweets <- read_twitter_csv("data/rstats_tweets.csv.gz") %>%
  mutate(created_at = as_datetime(created_at))
```


```{r time_data, include=FALSE, cache=TRUE}
count_timeseries <- rstats_tweets %>%
  ts_data(by = "hours")

tweets_week <- rstats_tweets %>%
  filter(date(created_at) %within% interval(floor_date(today(), "week"), today()))

tweets_today <- rstats_tweets %>%
  filter(date(created_at) == today())
```


```{r numbers, include=FALSE, cache=TRUE}
number_of_unique_tweets <- get_unique_value(rstats_tweets, text)

number_of_unique_tweets_today <-
  get_unique_value(tweets_today, text)

number_of_tweeters_today <- get_unique_value(tweets_today, user_id)

number_of_likes <- rstats_tweets %>%
  pull(favorite_count) %>%
  sum()
```


```{r rankings_data, include=FALSE, cache=TRUE}
top_tweeters <- rstats_tweets %>%
  group_by(user_id, screen_name, profile_url, profile_image_url) %>%
  summarize(engagement = (sum(retweet_count) * 2 + sum(favorite_count)) / n()) %>%
  ungroup() %>%
  slice_max(engagement, n = 10, with_ties = FALSE)

top_tweeters_format <- top_tweeters %>% 
  mutate(
    profile_url = stringr::str_glue("https://twitter.com/{screen_name}"),
    screen_name = stringr::str_glue('@{screen_name}'),
    engagement = formattable::color_bar("#a3c1e0", formattable::proportion)(engagement)
  ) %>%
  select(screen_name, engagement)

top_hashtags <- rstats_tweets %>%
  tidyr::separate_rows(hashtags, sep = " ") %>%
  count(hashtags) %>%
  filter(!(hashtags %in% c("rstats", "RStats"))) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  mutate(
    number = formattable::color_bar("plum", formattable::proportion)(n),
    hashtag = stringr::str_glue(
      '#{hashtags}'
    ),
  ) %>%
  select(hashtag, number)

word_banlist <-  c("t.co", "https", "rstats")
top_words <- rstats_tweets %>%
  select(text) %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words) %>%
  filter(!(word %in% word_banlist)) %>%
  filter(nchar(word) >= 4) %>% 
  count(word, sort = TRUE) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  select(word, n)

top_co_hashtags <- rstats_tweets %>% 
  unnest_tokens(bigram, hashtags, token = "ngrams", n = 2) %>% 
  tidyr::separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% c(stop_words$word, word_banlist)) %>% 
  filter(!word2 %in% c(stop_words$word, word_banlist)) %>% 
  count(word1, word2, sort = TRUE) %>% 
  filter(!is.na(word1) & !is.na(word2)) %>% 
  slice_max(n, n = 100, with_ties = FALSE)

top_locations <- rstats_tweets %>%
  filter(!is.na(location) & location != "#rstats") %>%
  distinct(user_id, .keep_all = TRUE) %>%
  mutate(location = str_replace_all(location, "London$", "London, England")) %>% 
  count(location) %>%
  slice_max(n, n = 10, with_ties = FALSE)
```


Home {data-icon="ion-home"}
====

Row
-----------------------------------------------------------------------

### Tweets Today

```{r tweets_today}
valueBox(number_of_unique_tweets_today, icon = "fa-comment-alt", color = "plum")
```

### Tweeters Today

```{r tweeters_today}
valueBox(number_of_tweeters_today, icon = "fa-user", color = "peachpuff")
```

### #rstats Likes

```{r likes}
valueBox(number_of_likes, icon = "fa-heart", color = "palevioletred")
```

### #rstats Tweets

```{r unique_tweets}
valueBox(number_of_unique_tweets, icon = "fa-comments", color = "mediumorchid")
```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Tweet volume

```{r tweet_volume}
plot_tweet_volume(count_timeseries)
```

### Tweets by Hour of Day

```{r tweets_by_hour}
plot_tweet_by_hour(rstats_tweets)
```

Row
-----------------------------------------------------------------------

### 💗 Most Liked Tweet Today {.tweet-box}

```{r most_liked}
most_liked_url <- tweets_today %>%
  slice_max(favorite_count, with_ties = FALSE)

get_tweet_embed(most_liked_url$screen_name, most_liked_url$status_id)
```

### ✨ Most Retweeted Tweet Today {.tweet-box}

```{r most_rt}
most_retweeted <- tweets_today %>%
  slice_max(retweet_count, with_ties = FALSE)

get_tweet_embed(most_retweeted$screen_name, most_retweeted$status_id)
```

### 🎉 Most Recent {.tweet-box}

```{r most_recent}
most_recent <- tweets_today %>%
  slice_max(created_at, with_ties=FALSE)

get_tweet_embed(most_recent$screen_name, most_recent$status_id)
```

Rankings {data-icon="ion-arrow-graph-up-right"}
=========

Row
-----------------------------------------------------------------------

### Top Tweeters

```{r top_tweeters}
top_tweeters_format %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("User", "Engagement/Tweet "),
    table.attr = 'class = "table"'
  )
```

Where Engagement is `RT * 2 + Favourite`

### Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters
that also have the hashtag.

```{r top_tweeters_net}
edgelist <-
  network_data(rstats_tweets %>% unflatten(), "reply,quote")
nodelist <- attr(edgelist, "idsn") %>%
  bind_cols()

top_edges <- edgelist %>%
  filter((from %in% top_tweeters$user_id) |
           (to %in% top_tweeters$user_id))

top_nodes <- nodelist %>%
  filter((id %in% top_edges$from) | (id %in% top_edges$to)) %>%
  mutate(is_top = ifelse((id %in% top_tweeters$user_id), "yes", "no"),
         size = 10)

e_charts() %>%
  e_graph() %>%
  e_graph_nodes(top_nodes, id, sn, size, category = is_top, legend = FALSE) %>%
  e_graph_edges(top_edges, from, to) %>%
  e_tooltip()
```

Row
-----------------------------------------------------------------------

### Top Words

```{r top_words}
top_words %>%
  e_charts(word) %>%
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of occurrences")
```

### Top Locations

```{r top_locations}
top_locations %>% 
  mutate(location = str_wrap(location, 9)) %>% 
  e_charts(location) %>% 
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of users from location")
```


Row
-----------------------------------------------------------------------

### Top Hashtags

```{r top_hashtags}
top_hashtags %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("Hashtag", "Count"),
    table.attr = 'class = "table"'
  )
```

Excluding `#rstats` and similar variations

### Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

```{r co_hashtags}
top_co_hash_nodes <- tibble(
  nodes = c(top_co_hashtags$word1, top_co_hashtags$word2)
) %>% 
  distinct()

e_chart() %>% 
  e_graph() %>% 
  e_graph_nodes(top_co_hash_nodes, nodes, nodes, nodes) %>% 
  e_graph_edges(top_co_hashtags, word1, word2) %>% 
  e_modularity()
```


Data {data-icon="ion-stats-bars"}
==============

### Tweets in the current week {.datatable-container}

```{r datatable}
tweets_week %>%
  select(
    status_url,
    created_at,
    screen_name,
    text,
    retweet_count,
    favorite_count,
    mentions_screen_name
  ) %>%
  mutate(
    status_url = stringr::str_glue("On Twitter")
  ) %>%
  datatable(
    .,
    extensions = "Buttons",
    rownames = FALSE,
    escape = FALSE,
    colnames = c("Timestamp", "User", "Tweet", "RT", "Fav", "Mentioned"),
    filter = 'top',
    options = list(
      columnDefs = list(list(
        targets = 0, searchable = FALSE
      )),
      lengthMenu = c(5, 10, 25, 50, 100),
      pageLength = 10,
      scrollY = 600,
      scroller = TRUE,
      dom = '<"d-flex justify-content-between"lBf>rtip',
      buttons = list('copy', list(
        extend = 'collection',
        buttons = c('csv', 'excel'),
        text = 'Download'
      ))
    )
  )
```